1. Technical Field
This invention relates to a method and system for improving the provision of information, and in particular to a method and system which improve the provision of information from a knowledge management system (KMS) provided for the use of a call centre agent.
2. Related Art
Many companies operate large call centres with agents dealing with a range of complex customer queries. Many of these requests require the agent to consult information databases to determine the correct response to a customer. It is desirable that the agent be able to complete the call as quickly as possible since this means fewer agents required within the call centre and hence reduced call centre costs.
The speed of the agent interaction depends on the agent's ability to:                1. convert the customer query into a specific database request;        2. know where relevant information in the database can be found; and        3. access that information through keyboard and mouse based user interfaces (UIs).        
All of these are dependent on the agent's knowledge and experience of the company's business and products and the information systems in use in the call centre. The knowledge is improved both by training and experience, however these take time and so additional costs are incurred before the agent reaches an effective performance level.
Two further issues restrict a call centre company's ability to develop sufficient knowledge of the company business, products and IT systems within an agent:                1. Agent Churn—industry average figures of 20% per annum. This means that at any one time 20% of agents are still inexperienced and hence unable to be able to perform tasks 1, 2, 3 at maximum efficiency. For agency staff the annual churn rate can increase to 100%; and        2. Product range—as companies expand their product ranges the ability of a single agent to translate a customer query into a specific database request becomes more difficult. This is exacerbated in virtual call centres where an agent needs to be able to handle requests regarding products from multiple companies.        
For companies operating call centres novice agents represent both reduced efficiency and a training cost associated with developing the agents. It is suggested that agents can typically take as long as 3 months to become completely effective.
The traditional approach to reducing call centre agent costs is to completely replace the agent with an automated Interactive Voice Response service utilising audio playout/record and Dual Tone Multi Frequency (DTMF) signals or Voice recognition. There are three classes of automation of a telephone enquiry depending on the level of voice/DTMF recognition in use:                1. Full Automation—In this situation the caller deals primarily with an IVR system that collects responses and queries databases automatically. Whilst this route can be the most effective at reducing call centre costs there can be a negative impact on customer satisfaction in particular situations where the customer query is complex.        2. Partial IVR automation—In some scenarios e.g. Directory Enquiry (DQ) automation the customer query can be broken down into a series of steps in which the early steps are within the capability of an automated IVR service, but where later steps exceed the capabilities thereof. Here IVR is used for the earlier steps and the data collected is then used to query the database. The call is then handed over to the agent part way through the database search and the agent takes over. This approach has the advantage of avoiding any potential negative effect on customer satisfaction since the caller always ends up with an agent. However the partial automation dialogue has the potential to cause negative reaction on the part of the caller and potential errors.        3. Store and Forward—To reduce agent handling time without affecting the accuracy of responses to callers, some solutions utilise Store and Forward IVR technology which utilises a very simple IVR system that is able to prompt the caller for data and record the caller responses. The responses are then played (often at speeded up rate) to the agent. The agent can then effectively perform the database search without having to have held a dialogue with the caller.        
All of the above techniques open the possibility of the caller realising that they have not dealt with a live agent since there is some element of dialogue recording, and it has been found that this can produce deleterious effects on the caller satisfaction. There is therefore a trend away from IVR solutions to reducing call centre costs, and towards attempts to improve call centre agent efficiency, whilst retaining the human agent as the principal customer facing element. Several existing Agent User Interface Techniques are known already, which have as their object the improvement of agent efficiency.
Keyboard shortcuts—This mechanism only addresses agent task 3 as identified earlier. Assuming that the agent has been able to determine the nature of the query and then subsequently translate that query into a specific set of information in a database then keyboard shortcuts provide a quick and convenient mechanism to access the specific information. The disadvantages are that agents can only remember a limited set of keyboard shortcuts. The greater the number of shortcuts in use the more difficult for the agent to remember and the greater number of keys involved in the short cut. The greater number of keys involved the lower the efficiency gain. Significant agent training is required before efficiency gains are realised.
Menu Key Ahead—Many call centre applications revolve around menu driven applications where the agent navigates through a menu hierarchy to find the point in the information database where the appropriate customer response is located or where customer data can be entered. As agents become more experienced they will remember the sequence of options required to access a particular menu. By allowing key ahead, menus may be bypassed by agents thereby increasing efficiency. The disadvantages are that menu hierarchies become difficult to change, in particular inserting a new menu is impossible since it will cause unpredictable behaviour for existing key ahead behaviour. Again agent training is required before efficiency gains are realised.
Frequently asked questions (FAQs)—This mechanism can address tasks 1, 2, and 3 as identified earlier. Many queries fall into one of perhaps 10 frequently asked questions the answer for which is a specific information page in the database. It is possible to offline analyse calls to the call centre and produce a FAQ which can be displayed to the agent, the list of frequent questions can be displayed to the agent along with a mechanism for selecting from the list via keyboard or mouse. The agent can make use of the FAQ list to help classify the caller query by comparison to other queries and to provide a means of directly accessing the information from the FAQ thereby avoiding the need to know where the information resides in the database hierarchy. FAQs can be made dynamic responding to the current top N queries to the call centre. The disadvantages are that the FAQ list is related to historical queries to the call centre and may not be relevant to the current call. In reality the number of FAQs that can be displayed is limited to perhaps 10 due to available screen space. So it is particularly appropriate where the nature queries does not change frequently and where the vast majority of queries fall into a limited number of categories.
Textual Search—Here the agent types in a search query to look up the relevant information, in a similar manner to performing a keyword search on an internet search engine The disadvantages are that the time taken by the agent to type in the query can be significant, and the time taken to perform the database search can be significant.
A-Z Indexes—Similar to FAQ here the UI contains an A-Z list which the agent may select upon to gain access to a list of products and services beginning with a particular letter. The disadvantages are that some letters may be the initial letter for many products and services and so produces a page with many details requiring the agent to scan through potentially long lists. Secondly the agent and the system need to agree on what is the first letter of the product or service. For example the product may be referred to as the BT onair 1250 or the Onair1250, and so could begin with B or O. If the index system uses only one then the agent may need to make two requests to find the right one, conversely if the system lists the product under both B and O then this increases the number of matches at any one query and so reduces the benefit of an indexing system.
To reduce the agents dependence on training and experience in using the various interface techniques as described above it is known to provide HTML based knowledge management systems (KMSs) which provide call centre agents with access to product and service information and comparison engines, using the agent interface techniques described above. Such HTML systems are accessed using a web browser such as Microsoft® Internet Explorer and a standard web style. A screen shot of an example system of this type developed by British Telecommunications plc (BT) is shown in FIG. 1.
The knowledge management system (KMS) is essentially an HTML website 10 generated automatically from a knowledge database 44 (shown in use in the embodiments of the invention in FIGS. 4 and 8). The website 10 contains information regarding BT products and services, procedures that agents must follow as well as links to other knowledge sources such as call pricing tools etc. The site is structured in principle as a hierarchy of web content for example “Products/analogue cordless phones/quartet 1100/features”.
The site provides several agent interface methods, will be apparent from FIG. 1. In particular, drop down menus 14 which are structured in accordance with the content hierarchy are provided, which allow an agent to navigate the information contained in the KMS by category. As will be apparent from FIG. 1, by selecting a particular category in the top-level menu a further menu is displayed, with additional sub-categories of information, the selection of which results in the display of a further menu if appropriate with additional sub-sub-categories, or the selection of the available information to be displayed. Any information selected is then accessed from the knowledge database and displayed in a display area 18 of the website 10.
In addition to the drop down menus, an A-Z index interface 16 is provided, which allows an agent to select a letter and have all the categories beginning with the selected letter displayed. The displayed categories may then be further selected to display any available information relating thereto in the display area 18.
Furthermore, a keyword search interface 12 is also provided, which allows an agent to enter a keyword which is then used in a standard keyword search of the information in the knowledge database 44. Any results are then displayed in the display area 18 for further selection by the agent.
Whilst the above description relates to the exemplary proprietary KMS developed by BT, other similar KMSs are also known. In particular, an example KMS exhibiting similar functions and maintained by easycar (UK) Ltd. was publicly available via the Internet before the priority date.
In other prior art, U.S. 2002019737 discloses a system which describes the use of an Automatic Speech Recognition system as an alternative to the GUI for entering information into a database search within a DQ call centre application.
The key features of U.S. 2002019737 is that the agent effectively acts as a mediator between the caller and an IVR system. The agent reformulates the caller verbal enquiry into a verbal form that is more easily dealt with by the IVR system. Fundamentally the IVR system is positioned between agent and database system. The system may be combined with a standard full automation IVR based database query performed at an earlier stage in the dialog after which the caller is transferred to the agent.
The system described has a number of disadvantages:—                1) A database search is entirely dependent on the operator speech, the caller speech is used only to validate an operator speech search but is not available to the agent independently;        2) the agent audio ideally must be muted in order to prevent the caller hearing the reformulated query, otherwise the possibility for caller confusion occurs;        3) (2) requires that additional switching hardware be installed to control the muting of audio;        4) whilst the agent repeats the audio enquiry to the agent IVR then there is a period of dead air in the dialogue;        5) since the database search is not performed until the agent has repeated the query then there is a loss of efficiency;        6) agent training is required in the use of the system; and        7) the system assumes that the agent is able to reformulate the query into a form which is more easily recognised than the original query from the caller, which may not be the case for inexperienced agents or complex queries.        
Therefore, although knowledge management systems as described above can be effective in reducing call handling times they tend to exhibit the problem that they are dependent on agents pulling information from the KM system rather than it being pushed to the agent, and hence they are still dependent on agents becoming familiar with the system and being able to navigate effectively therethrough. With the high employee turnover rates commonly prevalent within call centres, this familiarisation time can represent a significant cost. There is therefore a clear need for a system which proactively pushes relevant information to the call centre agent in order to overcome this problem.
However, systems are known in the art which listen to conversations and push relevant information to users in dependence on keywords within the conversation. An example of such a system is described in Jebara et al. “Tracking Conversational Context for machine Mediation of Human Discourse”, published on the Internet prior to the priority date of the present invention. Within this system a commercial grade speech recognizer is used to listen to a conversation between two or more people, and to spot keywords within the conversation which relate to the topic of the conversation, so as to identify the conversational context. The keywords are then used by a computer to suggest further topics of conversation for the two people, and these topics are subsequently displayed to the people on a screen.
Such a system requires a dedicated speech recogniser to listen to the entire conversation between the two people, and hence all of the speech recogniser resources are being used to monitor the single conversation. In a call centre scenario where potentially dozens of independent conversations are simultaneously ongoing such a system would require a separate speech recogniser resource for each conversation, with the result that a separate instantiation of a speech recogniser application would be required for each agent station. This requirement imposes technical drawbacks in terms of wasted speech recogniser resource, and economic drawbacks in terms of the separate software licences required being required for each agent station.